Detection and Classification of Melanoma Skin Cancer Analysis

Authors

  • Bapu Chendage  Department of Computer Applications, School of Computational Sciences, Punyashlok Ahilyadevi Holkar Solapur University, Solapur, Maharashtra, India
  • Rajivkumar Mente  Department of Computer Applications, School of Computational Sciences, Punyashlok Ahilyadevi Holkar Solapur University, Solapur, Maharashtra, India
  • Sunil Pawar  Department of Computer Applications, School of Computational Sciences, Punyashlok Ahilyadevi Holkar Solapur University, Solapur, Maharashtra, India

DOI:

https://doi.org/10.32628/CSEIT217130

Keywords:

Melanoma Cancer, K-Nearest Neighbor, Segmentation, Features, Malignant

Abstract

Nowadays, skin cancer becomes a very dangerous disease for human. Skin cancer is classified into many types such as Melanoma, Basal and squamous cell carcinoma. In all cancers, melanoma is the most dangerous and unpredictable disease. The detection of melanoma cancer in an early stage is beneficial for effective treatment. The detection of skin cancer contains four significant stages which are Pre-processing, Segmentation, Feature Extraction and Classification. The proposed study involves the collection of image database, preprocessing methods, segmentation using thresholding and classification using statistical features. The K-Nearest Neighbor (KNN) classifier is used for classification. The accuracy of KNN classifier for proposed research work is 93.4%.

References

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2021-02-28

Issue

Volume 7, Issue 1, January-February-2021

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Bapu Chendage, Rajivkumar Mente, Sunil Pawar, " Detection and Classification of Melanoma Skin Cancer Analysis" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 7, Issue 1, pp.150-154, January-February-2021. Available at doi : https://doi.org/10.32628/CSEIT217130